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Found 4 papers, 2 papers with code
Transfer Learning in Information Criteria-based Feature Selection
Our theoretical results indicate that, for any sample size in the target domain, the proposed TLCp estimator performs better than the Cp estimator by the mean squared error (MSE) metric in the case of orthogonal predictors, provided that i) the dissimilarity between the tasks from source domain and target domain is small, and ii) the procedure parameters (complexity penalties) are tuned according to certain explicit rules.
A Discussion on Practical Considerations with Sparse Regression Methodologies
Sparse linear regression is a vast field and there are many different algorithms available to build models.
OR-Gym: A Reinforcement Learning Library for Operations Research Problems
Reinforcement learning (RL) has been widely applied to game-playing and surpassed the best human-level performance in many domains, yet there are few use-cases in industrial or commercial settings.
The ALAMO approach to machine learning
Given a data set, the approach begins by building a low-complexity, linear model composed of explicit non-linear transformations of the independent variables.
